Methods and systems for updating models used for estimating glucose values

Abstract

Methods, systems and non-transient computer-readable media are provided for updating models used for estimating glucose values. For example, technologies are provided for updating an existing population model for estimating glucose values for a population of users to generate a new updated population model for a subset of users of the population of users. As another example, technologies are provided for updating an existing personalized model for estimating glucose values to generate a new updated personalized model that is personalized for a particular user.

What is claimed is: 
     
         1 . A method for updating an existing population model for estimating glucose values for a population of users to generate a new updated population model for a subset of users of the population of users, the method comprising:
 selecting, from a set of population data, selected population data for a subset of users;   training the existing population model based on the selected population data to generate the new updated population model;   performing a plausibility testing process to determine whether an estimated glucose response of the new updated population model changes in a physiologically appropriate manner in response to predetermined inputs being processed by the new updated population model, wherein the estimated glucose response comprises estimates of glucose values for the subset of users;   applying a predetermined testing dataset to the new updated population model and the existing population model;   comparing an estimated glucose response of the existing population model to the predetermined testing dataset to the estimated glucose response of the new updated population model to the predetermined testing dataset to determine whether the estimated glucose response of the new updated population model provides more accurate estimates of glucose values for the subset of users; and   replacing the existing population model with the new updated population model for usage with the subset of users in response to determining that the estimated glucose response of the new updated population model provides more accurate estimates of glucose values for the subset of users.   
     
     
         2 . A method according to  claim 1 , wherein selecting comprises:
 selecting, from the set of population data, the selected population data for the subset of users that share at least one of common user characteristics and common therapy criteria.   
     
     
         3 . A method according to  claim 1 , further comprising:
 performing a calibration point testing process on the new updated population model by evaluating performance of the new updated population model at different calibration intervals and determining which calibration interval is optimal for the new updated population model,   wherein each calibration interval is specified as a number of time units that define how often the new updated population model needs to be calibrated using one or more blood glucose values as an input to the new updated population model.   
     
     
         4 . A method according to  claim 3 , wherein evaluating performance of the new updated population model at different calibration intervals, comprises:
 at each calibration interval that is tested:   determining whether the new updated population model satisfies performance criteria when it is calibrated at that calibration interval.   
     
     
         5 . A method according to  claim 4 , wherein determining whether the new updated population model satisfies performance criteria, comprises:
 at each calibration interval that is tested: determining a performance score for the new updated population model when it is calibrated at that calibration interval, wherein the performance score is indicative of accuracy of glucose estimates produced by the new updated population model when the new updated population model is calibrated at that calibration interval; and determining whether that performance score is greater than or equal an error threshold; and   wherein determining which calibration interval is optimal for the new updated population model, comprises:   selecting, from a group of calibration intervals that are determined to have a performance score that is greater than or equal the error threshold, the one of the calibration intervals having the greatest duration as an optimized calibration interval to be used in conjunction with that new updated population model, wherein the optimized calibration interval indicates how often blood glucose value is to be provided as input to that new updated population model to achieve an acceptable level of accuracy in estimating glucose values for the population of users.   
     
     
         6 . A method according to  claim 3 , further comprising:
 repeating the steps of: selecting a subset of population data, training the existing population model, and performing the calibration point testing process to iteratively update a most recently updated population model for estimating the glucose values for a different subset of users of the population of users.   
     
     
         7 . A method according to  claim 1 , further comprising:
 implementing the new updated population model for the subset of users in response to determining the estimated glucose response of the new updated population model provides more accurate estimates of glucose values for the subset of users.   
     
     
         8 . A method according to  claim 1 , wherein performing the plausibility testing process, comprises:
 determining whether the estimated glucose response output of the new updated population model in response to the predetermined inputs being processed by the new updated population model is within an error threshold of an expected glucose response to the predetermined inputs; and   discarding the new updated population model when the estimated glucose response output of the new updated population model is not within the error threshold of the expected glucose response.   
     
     
         9 . A method according to  claim 1 , wherein the population data comprises historical data for each particular user of the population of particular users, comprising one or more of:
 data from a glucose monitoring device associated with the particular user;   data regarding consumption of macronutrients by the particular user; and   contextual activity data associated with the particular user.   
     
     
         10 . A method according to  claim 1 , wherein at least some of the population data that is selected to be evaluated for the subset of the population of particular users is acquired after the existing population model was generated. 
     
     
         11 . A system for updating an existing population model for estimating glucose values for a population of users to generate a new updated population model for a subset of users of the population of users, comprising:
 one or more hardware-based processors configured by machine-readable instructions to:   select, from a set of population data, selected population data for a subset of users;   train the existing population model based on the selected population data to generate the new updated population model;   perform a plausibility testing process to determine whether an estimated glucose response of the new updated population model changes in a physiologically appropriate manner in response to predetermined inputs being processed by the new updated population model, wherein the estimated glucose response comprises estimates of glucose values for the subset of users;   apply a predetermined testing dataset to the new updated population model and the existing population model;   compare an estimated glucose response of the existing population model to the predetermined testing dataset to the estimated glucose response of the new updated population model to the predetermined testing dataset to determine whether the estimated glucose response of the new updated population model provides more accurate estimates of glucose values for the subset of users; and   replace the existing population model with the new updated population model for usage with the subset of users in response to determining that the estimated glucose response of the new updated population model provides more accurate estimates of glucose values for the subset of users.   
     
     
         12 . A method for updating an existing personalized model for estimating glucose values to generate a new updated personalized model that is personalized for a particular user, the method comprising:
 using an existing population model to initialize parameters of an existing personalized model;   training the existing personalized model, based on new user data for a particular user that reflects physiology of the particular user, to adapt the existing personalized model and generate a new updated personalized model for the particular user;   performing a plausibility testing process to determine whether an estimated glucose response of the new updated personalized model changes in a physiologically appropriate manner in response to modified user data for the particular user when it is processed by the new updated personalized model;   applying a predetermined testing dataset to the new updated personalized model and to the existing personalized model;   comparing an estimated glucose response of the existing personalized model to the predetermined testing dataset to an estimated glucose response of the new updated personalized model to the predetermined testing dataset to determine whether the new updated personalized model provides more accurate estimates of glucose values for the particular user; and   replacing the existing personalized model with the new updated personalized model for usage with the particular user in response to determining that the estimated glucose response of the new updated personalized model provides more accurate estimates of glucose values for the particular user.   
     
     
         13 . A method according to  claim 12 , further comprising:
 performing a calibration point testing process on the new updated personalized model by evaluating performance of the new updated personalized model at different calibration intervals and determining which calibration interval is optimal for the new updated personalized model,   wherein each calibration interval is specified as a number of time units that define how often the new updated personalized model needs to be calibrated using one or more blood glucose values as an input to the new updated personalized model.   
     
     
         14 . A method according to  claim 13 , wherein evaluating performance of the new updated personalized model at different calibration intervals, comprises:
 at each calibration interval that is tested:   determining whether the new updated personalized model satisfies performance criteria when it is calibrated at that calibration interval.   
     
     
         15 . A method according to  claim 13 , wherein determining whether the new updated personalized model satisfies performance criteria, comprises:
 at each calibration interval that is tested: determining a performance score for the new updated personalized model when it is calibrated at that calibration interval, wherein the performance score is indicative of accuracy of glucose estimates produced by the new updated personalized model when the new updated personalized model is calibrated at that calibration interval; and determining whether that performance score is greater than or equal an error threshold; and   wherein determining which calibration interval is optimal for the new updated personalized model, comprises:   selecting, from a group of calibration intervals that are determined to have a performance score that is greater than or equal the error threshold, the one of the calibration intervals having the greatest duration as an optimized calibration interval to be used in conjunction with that new updated personalized model, wherein the optimized calibration interval indicates how often blood glucose value is to be provided as input to that new updated personalized model to achieve an acceptable level of accuracy in estimating glucose values for the particular user.   
     
     
         16 . A method according to  claim 13 , further comprising:
 after updating the existing personalized model with the new updated personalized model, repeating the steps of: training the existing personalized model, performing the plausibility testing process, and performing the calibration point testing process to iteratively update, based on other new user data for the particular user, a most recently updated personalized model for estimating the glucose values for the particular personalized user.   
     
     
         17 . A method according to  claim 12 , wherein performing the plausibility testing process, comprises:
 determining, in response to the modified user data for the particular user being processed by the new updated personalized model, whether the estimated glucose response output of the new updated personalized model is within an error threshold of an expected glucose response to the modified user data for the particular user; and   discarding the new updated personalized model when the estimated glucose response output of the new updated personalized model is not within the error threshold of the expected glucose response.   
     
     
         18 . A method according to  claim 12 , further comprising:
 iteratively updating the existing population model for estimating glucose values for a population of particular users to generate a new updated population model that improves performance of the existing population model by providing improved estimates of glucose values for a subset of the population of particular users, wherein iteratively updating the existing population model, comprises:   selecting, from a set of population data, selected population data for a subset of users that share at least one of common user characteristics and common therapy criteria; and   training the existing population model based on the selected population data for the subset of users to generate the new updated population model.   
     
     
         19 . A system for updating an existing personalized model for estimating glucose values to generate a new updated personalized model that is personalized for a particular user, the system comprising:
 one or more hardware-based processors configured by machine-readable instructions to:   use an existing population model to initialize parameters of an existing personalized model;   train the existing personalized model, based on new user data for a particular user that reflects physiology of the particular user, to adapt the existing personalized model and generate a new updated personalized model for the particular user;   perform a plausibility testing process to determine whether an estimated glucose response of the new updated personalized model changes in a physiologically appropriate manner in response to modified user data for the particular user when it is processed by the new updated personalized model;   apply a predetermined testing dataset to the new updated personalized model and to the existing personalized model;   compare an estimated glucose response of the existing personalized model to the predetermined testing dataset to an estimated glucose response of the new updated personalized model to the predetermined testing dataset to determine whether the new updated personalized model provides more accurate estimates of glucose values for the particular user; and   replace the existing personalized model with the new updated personalized model for usage with the particular user in response to determining that the estimated glucose response of the new updated personalized model provides more accurate estimates of glucose values for the particular user.